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Jing Zhao

Bio: Jing Zhao is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Signal timing & Poison control. The author has an hindex of 22, co-authored 86 publications receiving 1478 citations. Previous affiliations of Jing Zhao include Chinese Ministry of Education & Delft University of Technology.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: In this article, the authors examined the phosphorylation status of two other constitutive sites, serines 955 and 973, and concluded that the mechanisms of regulation of Ser910/935/955/973 are similar and physiologically relevant.
Abstract: Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson's disease. An amino terminal cluster of constitutively phosphorylated residues, serines 860, 910, 935, 955, and 973, appears to be biologically relevant. Phosphorylation of serines 910 and 935 is regulated in response to LRRK2 kinase activity and is responsible for interaction with 14-3-3 and maintaining LRRK2 in a non-aggregated state. We examined the phosphorylation status of two other constitutive phosphorylation sites, serines 955 and 973. Treatment of LRRK2 expressing cells with the selective LRRK2 inhibitor LRRK2-IN1 revealed that, like Ser910/Ser935, phosphorylation of Ser955 and Ser973 is disrupted by acute inhibition of LRRK2 kinase activity. Additionally, phosphorylation of Ser955 and 973 is disrupted in the context of several Parkinson's disease associated mutations [R1441G/C, Y1699C, and I2020T]. We observed that modification of Ser973 is dependent on the modification of Ser910/Ser935. Ser955Ala and Ser973Ala mutations do not induce relocalization of LRRK2; however, all phosphomutants exhibited similar localization patterns when exposed to LRRK2-IN1. We conclude that the mechanisms of regulation of Ser910/935/955/973 phosphorylation are similar and physiologically relevant. These sites can be utilized as biomarkers for LRRK2 activity as well as starting points for the elucidation of upstream and downstream enzymes that regulate LRRK2.

89 citations

Journal ArticleDOI
TL;DR: The study concludes that business as usual will lead to a continuing rapid deterioration of the water quality of the Han River and the effectiveness of the 2 proposed mitigation projects, the 22 wastewater treatment plants (WWTPs) and the Yangtze-Han Water Diversion Project.

88 citations

Journal ArticleDOI
TL;DR: PP1 is revealed as the physiological LRRK2 phosphatase, responsible for L RRK2 dephosphorylation observed in PD mutant LRRk2 and after LRR K2 kinase inhibition.
Abstract: A cluster of phosphorylation sites in LRRK2 (leucine-rich repeat kinase 2), including Ser910, Ser935, Ser955 and Ser973, is important for PD (Parkinson's disease) pathogenesis as several PD-linked LRRK2 mutants are dephosphorylated at these sites. LRRK2 is also dephosphorylated in cells after pharmacological inhibition of its kinase activity, which is currently proposed as a strategy for disease-modifying PD therapy. Despite this importance of LRRK2 dephosphorylation in mutant LRRK2 pathological mechanism(s) and in LRRK2's response to inhibition, the mechanism by which this occurs is unknown. Therefore we aimed to identify the phosphatase for LRRK2. Using a panel of recombinant phosphatases, we found that PP1 (protein phosphatase 1) efficiently dephosphorylates LRRK2 in vitro. PP1 activity on LRRK2 dephosphorylation was confirmed in cells using PP1 inhibition to reverse LRRK2 dephosphorylation induced by the potent LRRK2 kinase inhibitor LRRK2-IN1 as well as in R1441G mutant LRRK2. We also found that PP1 and LRRK2 can form a complex in cells. Furthermore, we observed that PP1 inhibition modulates LRRK2's cellular phenotype by reducing skein-like LRRK2-positive structures associated with dephosphorylation. In conclusion, the present study reveals PP1 as the physiological LRRK2 phosphatase, responsible for LRRK2 dephosphorylation observed in PD mutant LRRK2 and after LRRK2 kinase inhibition.

80 citations

Journal ArticleDOI
TL;DR: This study proposes a novel method to model the trajectories of vehicles in two-dimensional space and speed based on optimal control theory, which assumes drivers schedule their driving behavior, including the steering and acceleration, to minimize the predicted costs.
Abstract: Modeling traffic flow at intersections is essential for the design, control, and management of intersections. A challenging feature of microscopic modeling vehicular movement at intersections is that drivers can choose among an infinite number of alternative traveling paths and speeds. This makes it fundamentally different from structured straight road sections with lanes. This study proposes a novel method to model the trajectories of vehicles in two-dimensional space and speed. Based on optimal control theory, it assumes drivers schedule their driving behavior, including the steering and acceleration, to minimize the predicted costs. The costs contain the running costs, which consist of the travel time and driving smoothness (longitudinally and laterally), and the terminal cost, which penalizes the deviations from the desired final state. Different than conventional methods, the vehicle motion dynamics are formulated in distance rather than in time. The model is solved by an iterative numerical solution algorithm based on the Minimum Principle of Pontryagin. The descriptive power, plausibility, and accuracy of the proposed model are investigated by comparing the calculated results under several cases, which can be solved from symmetry or analytically. The proposed model is further calibrated and validated using empirical trajectory data, and the quality of the predicted trajectory is confirmed. Qualitatively, the optimal trajectory changes in the range of the shortest path and smoothest path under different weights of the running cost. The proposed model can be used as a starting point and extended with more considerations of intersection operation in the real world for future applications.

79 citations

Journal ArticleDOI
TL;DR: Regression analysis indicates that human activity was possibly the main cause of the decline of runoff after 1970 and the main driving factors for the changes in the Wei River Basin are identified.

74 citations


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01 Jan 2016
TL;DR: The logistic regression a self learning text is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading logistic regression a self learning text. As you may know, people have search hundreds times for their favorite books like this logistic regression a self learning text, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. logistic regression a self learning text is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the logistic regression a self learning text is universally compatible with any devices to read.

999 citations